Fourier correlation techniques are very efficient for protein docking using measures of surface complementarity as the target function. However, in addition to near-native conformations, the method yields an enormous number of false positives (i.e., conformations with good score but large RMSD). Substantial progress has been made in developing post-processing methods that can rank the docked conformations and select the ones close to the native, but the rigid body nature of protein docking still remains a limitation. Post-processing helps if there is a strong shape complementarity as in enzyme-inhibitor complexes. However, the interface is less well-packed in antibody-antigen and many other complexes, in which polar interactions and salt bridges are more important for binding. For these complexes, Fourier correlation techniques produce fewer hits, discrimination of the near-native docked structures becomes difficult due to the lower affinity, and the results are very sensitive to small perturbations in the coordinates of the component proteins. The general goal of this proposal is to extend the power of multistage docking beyond the complexes primarily stabilized by shape complementarity. This will be achieved (1) by developing robust discrimination methods, based on the clustering of the docked conformations; (2) by simultaneous flexible refinement of the retained clusters that will be able to find and refine even low quality hits; (3) by adjusting the conformation of the most important surface side chains, based on conformational statistics from nanosecond molecular dynamics simulations with explicit solvent; and (4) by integrating results from the docking of multiple protein structures that have been generated in the previous step, and thus differ in the conformations of some key side chains. Preliminary results show that these strategies will substantially improve docking results for relatively weak complexes that frequently play important roles in immune recognition, signal transduction, and cell cycle control.